Linda Nozick is a professor in Cornell’s School of Civil and Environmental Engineering. She served as director of the school for more than a decade. Prior to that role, Nozick was the director of the college’s Systems Engineering Program, which she co-founded. Nozick has been the recipient of several awards including a CAREER award from the National Science Foundation and a Presidential Early Career Award for Scientists and Engineers from President Clinton for “the development of innovative solutions to problems associated with the transportation of hazardous waste.” She is a past U.S. Presidential appointee to the U.S Nuclear Waste Technical Review Board. Nozick has authored over 100 peer-reviewed publications, many focused on transportation, the movement of hazardous materials, and the modeling of critical infrastructure systems. Nozick holds a B.S. in systems analysis and engineering from the George Washington University and a M.S.E and Ph.D. in systems engineering from the University of Pennsylvania.
Neural Networks and Machine LearningCornell Course
Course Overview
Neural networks, a nonlinear supervised learning modeling tool, have become hugely popular within the last two decades because they have been successfully applied to a wide range of problems, including automatic language processing, image classification, object detection, speech recognition, and pattern recognition. They are mathematical models that are loosely built up based on an analogy to the interconnected neuron in the brain. They take in a vector or matrix of input data and output either a classification value or an approximation to a functional value. The beauty is that the relationships between the inputs and outputs can be highly non-linear and complex.
In this course, you will explore the mechanics of neural networks and the intricacies involved in fitting them to data for prediction. Using packages in the free and open-source statistical programming language R with real-world data sets, you will implement these techniques. The focus will be on making these methods accessible for you in your own work.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Understanding Data Analytics
- Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
- Finding Patterns in Data Using Cluster and Hotspot Analysis
- Regression Analysis and Discrete Choice Models
- Supervised Learning Techniques
Key Course Takeaways
- Examine common architectures and activation functions for neural networks
- Identify how to optimize the parameters in a neural network
- Make predictions using neural networks in R
- Practice deep learning using R
- Apply ideas for cross-validation for neural network model development and validation
- Tune parameters in a neural network using a grid search
- Use the package Lime in R to recognize which variables are driving the recommendations your neural network is making
How It Works
Course Author
Who Should Enroll
- Current and aspiring data scientists
- Analysts
- Engineers
- Researchers
- Technical managers
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